ABSTRACT Despite the very large amounts spent by some individuals and households on long-term care, the market for long-term care insurance (LTCI) in the United States is not well developed. The overall goal of this project is to find the reasons why. The research is framed in the context of life-cycle models of spending that cover such health-related components as out-of-pocket spending on health care, long-term care in nursing homes, and LTCI. Two complementary modeling approaches will be employed: the first constructs a rich simulation model based on empirical estimates of initial conditions, taking into account all financial resources of the household and relevant transition rates (spending, health, mortality, long-term care status) stratified by marital status, sex and education. These estimates are used to simulate a person's risk of exhausting wealth before death, with and without LTCI. The fraction of the population for whom the risk of exhausting wealth is reduced by LTCI below some defined level would provide an estimate of the additional demand for LTCI in a market in which the participants behaved rationally to maximize their welfare. The second modeling approach will construct and estimate a dynamic programming model for married and single persons. Both models will be used to simulate the effects of different long-term care insurance policies, including the provisions of the CLASS Act. The research combines data from the Health and Retirement Study, including the detailed spending data from its supplemental study, the Consumption and Activities Mails survey, with newly collected data on consumer preferences to better understand behavioral or informational barriers (e.g., misinformation about the probabilities of nursing-home entry) to the purchase of LTCI. It also includes analyses of how individuals' information about Medicaid rules and private insurance interacts with individuals' private information about their own likelihood of needing long-term care to influence people's decisions to purchase LTCI. These results are combined with estimates of how insurance characteristics affect market penetration across subpopulations to design insurance packages that are better matched to the needs of different market segments, potentially increasing take-up rates.